In fact, startups focusing on the use of AI in drug design and development have seen significant investment of around $5.2 billion in recent years.

Information engines

Information engines are fundamental machines behind drug discovery applications, serving as the basic information aggregator and synthesizer layer on which the other applications can draw their insights, conclusions, and prescriptive functions. Scientists use these engines to update and aggregate information and pull out the data most likely to be relevant for a specific purpose.

An advanced information engine integrates data from multiple sources, including the following:

• Scientific research publications
• Medical records
• Doctors journals
• Biomedical information, such as known drug targets, ligand information, and disease specific information
• Historical clinical trial data
• Patent information from molecules currently being investigated at global pharma companies
• Proprietary enterprise data from internal research studies at the individual pharma client
• Genomic sequencing data
• Radiology imaging data
• Cohort data
• Other real-world evidence, such as society and environmental data

AI for drug design

AI-based drug design applications are involved directly with the molecular structure of the drugs. They draw data and insights from information engines to help generate novel drug candidates, to validate or optimize drug candidates, or to repurpose existing drugs for new therapeutic areas.

Ulrik Kristensen, PhD, is a senior market analyst at Signify Research.

For target identification, machine learning is first used to predict potential disease targets, then an AI triage typically orders targets based on chemical opportunity, safety, and druggability to identify the most promising targets. This information is then fed into the drug design application, which optimizes the compounds with desired properties before they are selected for synthesis. Experimental data from the selected compounds can then be fed back into the model to generate additional data for optimization.

For drug repurposing, existing drugs approved for specific therapeutic areas are compared against possible similar pathways and targets in alternative diseases, which creates opportunity for additional revenue from already developed pharmaceuticals. It also gives potential relief for rare disease areas where developing a new compound wouldn't be profitable.

Additionally, keeping repurposing in mind during development of a new drug as opposed to having a disease-specific mindset may result in more profitable multipurpose pharmaceuticals entering the market in the coming years.

AI in the fight against coronavirus

Recent investments in AI for drug development have given startups the manpower and resources necessary to develop their technologies. The funding has been spent on significantly expanding and building capacity, as the total number of employees across these AI startups is now close to 10,000 globally.

A strong focus for startup vendors is to create tight partnerships with the pharmaceutical industry. For many still in the early product development stages, this gives them the ability to test and optimize their solutions and to create a proof of concept as a basis for additional deals.

For the more established startups, partnerships with the pharmaceutical industry turn the initial investments into revenue in the form of subscription or consulting charges, potential milestone payments for new drug candidates, preparing the company for further investments, initial public offerings, acquisitions, or continued success as a separate company. Pharmaceutical companies with high numbers of publicly announced AI partnerships include AstraZeneca, GSK, Sanofi, Merck, Janssen, and Pfizer.

Now, many AI startups are primed to explore partnership opportunities or showcase their capabilities. The COVID-19 pandemic is therefore an important test for many of these vendors, giving them the opportunity to demonstrate the value of their technologies and hopefully help the world get through this crisis faster.

Understanding the protein structures on the coronavirus capsule can form the basis of a drug or vaccine. Researchers at Google's DeepMind have been using the artificial intelligence engine to quickly predict the structure of six proteins linked to the novel coronavirus, and although they have not been experimentally verified, these proteins may still contribute to the research ultimately leading to therapeutics.

Hong Kong-based Insilico Medicine took the next step in finding possible treatments, using AI algorithms to design new molecules that could potentially limit the virus's ability to replicate. Using existing data on the similar virus that caused the SARS outbreak in 2003, they published the structures of six new molecules that could potentially treat COVID-19.

Also, Germany-based Innoplexus has used its drug discovery information engine to design a novel molecule candidate with high binding affinity to a target protein on the novel coronavirus while maintaining drug-likeness criteria such as bioavailability, absorption, toxicity etc. Other AI players following similar strategies to identify new targets and molecules include Pepticom, Micar Innovation, Acellera, MAbSilico, InveniAI and Iktos, and further initiatives are announced daily.

Although AI can help researchers identify targets and potential designs, clinical testing and regulatory approval will still take about a year. So, while waiting for a vaccine or a new drug to be developed, other teams are looking at existing drugs on the market that could be repurposed to treat COVID-19.

BenevolentAI used its machine learning-based information engine to search for already approved drugs that could block the infection process. After analyzing chemical properties, medical data, and scientific literature, company researchers identified baricitinib, typically used to treat moderate and severe rheumatoid arthritis, as a potential candidate to treat COVID-19. The theory is that the drug would prevent the SARS-CoV-2 virus from entering the cells by inhibiting endocytosis, and thereby in combination with antiviral drugs reduce viral infectivity and replication and prevent the inflammatory response which causes some of the COVID-19 symptoms.

Although a lot is happening in the industry right now and there are many suggestions as to what might work as a therapy for COVID-19, the scientific and medical communities as well as regulators will not neglect the scientific method. Suggestions and new ideas are essential for progress, but so is rigor in testing and validation of hypotheses. A systematic approach, fueled by accelerated findings using AI and bright minds in collaboration, will lead to a better outcome.

Ulrik Kristensen, PhD, is a senior market analyst at Signify Research with eight years of experience with the healthtech industry. He is part of the Healthcare IT team and leads the research covering drug development, oncology, and genomics. Ulrik holds a Master of Science in Molecular Biology from Aarhus University and a doctorate from University of Strasbourg. He can be reached at ulrik.kristensen@signifyresearch.net.

Signify Research is an independent supplier of market intelligence and consultancy to the global healthcare technology industry. Its major coverage areas are healthcare IT, medical imaging, and digital health. Clients include technology vendors, healthcare providers and payers, management consultants, and investors. Signify Research is headquartered in Cranfield, U.K.

The comments and observations expressed do not necessarily reflect the opinions of The Science Advisory Board, nor should they be construed as an endorsement or admonishment of any particular vendor, analyst, industry consultant, or consulting group.